(Submitted on October 17, 2017; Revised on November 23, 2017; Accepted on December 29, 2017)

Abstract:

This paper proposed an algorithm that fused block-based algorithm (Radial Harmonic Fourier Moments, RHFMs) and SIFT algorithm (Scale Invariant Feature Transform, SIFT) for the duplicated forgery detection, also called copy-move forgery detection. It can effectively detect forgery image. Firstly, the SIFT algorithm is proposed to extract feature points from the pre-processing image. Subsequently, the nearest-neighbor (2NN) test, adaptive Euclidean distance and Random sample consensus (RANSAC) are applied to remove most of the mismatched feature points and get the candidate inlier matches. The affine matrixes based on the RANSAC are obtained by the candidate inlier matches. Then, Radial Harmonic Fourier Moments is proposed to extract invariances of the candidate inlier matches in circle blocks. The propagated criterion is calculated by affine matrix and circular feature of the inlier matches. The Simple Linear Iterative Clustering (SLIC) is presented to segment the host image into texture patches. Like pixel propagating, the circular block is slid in the corresponding texture blocks that the texture blocks contain the corresponding inlier matches to get more corresponding matches. Finally, some geometric image operations, such as dilation, are employed to eliminate the small holes or isolated pixels. A series of experiments showed that the proposed fusion algorithm can achieve superior performances than those of the moment invariant algorithms under various geometric transformations.